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A Machine-Learning Approach for Monitoring Water Distribution Networks (WDNs)

Author

Listed:
  • Roberto Magini

    (DICEA, Sapienza University of Rome, 00184 Rome, Italy)

  • Manuela Moretti

    (DICEA, Sapienza University of Rome, 00184 Rome, Italy)

  • Maria Antonietta Boniforti

    (DICEA, Sapienza University of Rome, 00184 Rome, Italy)

  • Roberto Guercio

    (DICEA, Sapienza University of Rome, 00184 Rome, Italy)

Abstract

The knowledge of the simultaneous nodal pressure values in a water distribution network (WDN) can favor its correct management, with advantages for both water utilities and end users, and guarantee higher sustainability in the use of the water resource. However, monitoring pressure in all the nodes is not feasible, so it can be useful to develop methods that allow us to estimate the whole pressure field based on data from a limited number of nodes. For this purpose, the work employed an artificial neural network (ANN) as a machine-learning regression algorithm. Uncertainty of water demand is modeled through scaling laws, linking demand statistics to the number of users served by each node. Three groups of demand scenarios are generated by using a Latin Hypercube Random Sampling with three different cross-correlations matrices of the nodal demands. Each of the corresponding groups of pressure scenarios is employed for the training of an ANN, whose performance parameter is preliminarily used to solve the sampling design for the WDN. Most of the so-derived monitoring nodes coincide in the three cases. The performance of each ANN appears to be strongly influenced by cross-correlation values, with the best results provided by the ANN relating to the most correlated demands.

Suggested Citation

  • Roberto Magini & Manuela Moretti & Maria Antonietta Boniforti & Roberto Guercio, 2023. "A Machine-Learning Approach for Monitoring Water Distribution Networks (WDNs)," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:2981-:d:1060319
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    References listed on IDEAS

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    1. Stefano Alvisi & Marco Franchini & Alberto Marinelli, 2003. "A Stochastic Model for Representing Drinking Water Demand at Residential Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(3), pages 197-222, June.
    2. Sara Nazif & Mohammad Karamouz & Massoud Tabesh & Ali Moridi, 2010. "Pressure Management Model for Urban Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(3), pages 437-458, February.
    3. Cristiana Bragalli & Claudia D’Ambrosio & Jon Lee & Andrea Lodi & Paolo Toth, 2015. "Optimizing the Design of Water Distribution Networks Using Mathematical Optimization," International Series in Operations Research & Management Science, in: Katta G. Murty (ed.), Case Studies in Operations Research, edition 127, chapter 9, pages 183-198, Springer.
    4. Dongwoo Jang & Hyoseon Park & Gyewoon Choi, 2018. "Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
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